We use a range of other multi-variate datasets for which no graph structure is known: Electricity,\(^,\)  Solar-energy,\(^,\) [2] Exchange-rate[2] and Traffic.\(^,\) [2]
Further, SWaT[1]} and WADI [2]} are datasets of sensors measuring water-treatment plants.
In the test split there are annotated anomalies where the creators tampered with the water treatment systems.
Therefore, SWaT and WADI were originally proposed as anomaly detection datasets (and e.g., used in the GDN paper);
however, since the respective training sets are free of anomalies, we use them for our forecasting experiments.
[1]archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014
[2]github.com/laiguokun/multivariate-time-series-data
[3]www.nrel.gov/grid/solar-power-data.html
[4]https://pems.dot.ca.gov
